The "Association Rules and Outliers Analysis" course introduces students to fundamental concepts of unsupervised learning methods, focusing on association rules and outlier detection. Participants will delve into frequent patterns and association rules, gaining insights into Apriori algorithms and constraint-based association rule mining. Additionally, students will explore outlier detection methods, with a deep understanding of contextual outliers. Through interactive tutorials and practical case studies, students will gain hands-on experience in applying association rules and outlier detection techniques to diverse datasets.
Association Rules Analysis
This course is part of Data Analysis with Python Specialization
Instructor: Di Wu
2,359 already enrolled
Included with
Recommended experience
What you'll learn
Understand the principles and significance of unsupervised learning methods, specifically association rules and outlier detection
Grasp the concepts and applications of frequent patterns and association rules in discovering interesting relationships between items.
Apply various outlier detection methods, including statistical and distance-based approaches, to identify anomalous data points.
Skills you'll gain
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There are 5 modules in this course
This week provides an introduction to unsupervised learning and association rules analysis. You will explore frequent itemsets, understanding their significance in discovering patterns in transactional data. You will also explore association rules, such as support, confidence, and lift metrics as key indicators of association rule quality.
What's included
2 videos4 readings1 assignment
This week we will briefly discuss association rule mining, such as closed and maxed patterns.
What's included
1 video1 assignment
This week focuses on the Apriori and FP Growth algorithm, a key method for efficient frequent itemset mining.
What's included
2 videos4 readings1 assignment1 discussion prompt
Throughout this week, you will explore the significance of outlier detection and its role in identifying unusual data points.
What's included
1 video2 readings1 assignment1 discussion prompt
The final week focuses on a comprehensive case study where you will apply association rule mining and outlier detection techniques to solve a real-world problem.
What's included
1 reading1 assignment1 discussion prompt
Instructor
Offered by
Recommended if you're interested in Data Analysis
O.P. Jindal Global University
University of Colorado Boulder
University of Illinois Urbana-Champaign
University of Colorado Boulder
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